Downregulation of endogenous nectin1 in human keratinocytes by herpes simplex virus 1 glycoprotein D excludes superinfection but does not affect NK cell function.

Many viruses downregulate their cognate receptors, facilitating virus replication and pathogenesis via processes that are not yet fully understood. In the case of herpes simplex virus 1 (HSV1), the receptor binding protein glycoprotein D (gD) has been implicated in downregulation of its receptor nectin1, but current understanding of the process is limited. Some studies suggest that gD on the incoming virion is sufficient to achieve nectin1 downregulation, but the virus-encoded E3 ubiquitin ligase ICP0 has also been implicated. Here we have used the physiologically relevant nTERT human keratinocyte cell type - which we have previously shown to express readily detectable levels of endogenous nectin1 - to conduct a detailed investigation of nectin1 expression during HSV1 infection. In these cells, nectin1, but not nectin2 or the transferrin receptor, disappeared from the cell surface in a process that required virus protein synthesis rather than incoming virus, but did not involve virus-induced host shutoff. Furthermore, gD was not only required but was sufficient for nectin1 depletion, indicating that no other virus proteins are essential. NK cells were shown to be activated in the presence of keratinocytes, a process that was greatly inhibited in cells infected with wild-type virus. However, degranulation of NK cells was also inhibited in ΔgD-infected cells, indicating that blocking of NK cell activation was independent of gD downregulation of nectin1. By contrast, a superinfection time-course revealed that the ability of HSV1 infection to block subsequent infection of a GFP-expressing HSV1 was dependent on gD and occurred in line with the timing of nectin1 downregulation. Thus, the role of gD-dependent nectin1 impairment during HSV infection is important for virus infection, but not immune evasion, which is achieved by other mechanisms.

Malaria seroepidemiology in very low transmission settings in the Peruvian Amazon.

Despite progress towards malaria reduction in Peru, measuring exposure in low transmission areas is crucial for achieving elimination. This study focuses on two very low transmission areas in Loreto (Peruvian Amazon) and aims to determine the relationship between malaria exposure and proximity to health facilities. Individual data was collected from 38 villages in Indiana and Belen, including geo-referenced households and blood samples for microscopy, PCR and serological analysis. A segmented linear regression model identified significant changes in seropositivity trends among different age groups. Local Getis-Ord Gi* statistic revealed clusters of households with high (hotspots) or low (coldspots) seropositivity rates. Findings from 4000 individuals showed a seropositivity level of 2.5% (95%CI: 2.0%-3.0%) for P. falciparum and 7.8% (95%CI: 7.0%-8.7%) for P. vivax, indicating recent or historical exposure. The segmented regression showed exposure reductions in the 40-50 age group (ß1 = 0.043, p = 0.003) for P. vivax and the 50-60 age group (ß1 = 0.005, p = 0.010) for P. falciparum. Long and extreme distance villages from Regional Hospital of Loreto exhibited higher malaria exposure compared to proximate and medium distance villages (p < 0.001). This study showed the seropositivity of malaria in two very low transmission areas and confirmed the spatial pattern of hotspots as villages become more distant.

Predictive value of ATN biomarker profiles in estimating disease progression in Alzheimer's disease dementia.

We aimed to evaluate the value of ATN biomarker classification system (amyloid beta [A], pathologic tau [T], and neurodegeneration [N]) for predicting conversion from mild cognitive impairment (MCI) to dementia. In a sample of people with MCI (n = 415) we assessed predictive performance of ATN classification using empirical knowledge-based cut-offs for each component of ATN and compared it to two data-driven approaches, logistic regression and RUSBoost machine learning classifiers, which used continuous clinical or biomarker scores. In data-driven approaches, we identified ATN features that distinguish normals from individuals with dementia and used them to classify persons with MCI into dementia-like and normal groups. Both data-driven classification methods performed better than the empirical cut-offs for ATN biomarkers in predicting conversion to dementia. Classifiers that used clinical features performed as well as classifiers that used ATN biomarkers for prediction of progression to dementia. We discuss that data-driven modeling approaches can improve our ability to predict disease progression and might have implications in future clinical trials.

MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide.

Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. In particular, MRI-derived brain age has been used as a comprehensive biomarker of brain health that can identify both advanced and resilient ageing individuals via deviations from typical brain ageing. Imaging signatures of various brain diseases, including schizophrenia and Alzheimer's disease, have also been identified using machine learning. Prior efforts to derive these indices have been hampered by the need for sophisticated and not easily reproducible processing steps, by insufficiently powered or diversified samples from which typical brain ageing trajectories were derived, and by limited reproducibility across populations and MRI scanners. Herein, we develop and test a sophisticated deep brain network (DeepBrainNet) using a large (n = 11 729) set of MRI scans from a highly diversified cohort spanning different studies, scanners, ages and geographic locations around the world. Tests using both cross-validation and a separate replication cohort of 2739 individuals indicate that DeepBrainNet obtains robust brain-age estimates from these diverse datasets without the need for specialized image data preparation and processing. Furthermore, we show evidence that moderately fit brain ageing models may provide brain age estimates that are most discriminant of individuals with pathologies. This is not unexpected as tightly-fitting brain age models naturally produce brain-age estimates that offer little information beyond age, and loosely fitting models may contain a lot of noise. Our results offer some experimental evidence against commonly pursued tightly-fitting models. We show that the moderately fitting brain age models obtain significantly higher differentiation compared to tightly-fitting models in two of the four disease groups tested. Critically, we demonstrate that leveraging DeepBrainNet, along with transfer learning, allows us to construct more accurate classifiers of several brain diseases, compared to directly training classifiers on patient versus healthy control datasets or using common imaging databases such as ImageNet. We, therefore, derive a domain-specific deep network likely to reduce the need for application-specific adaptation and tuning of generic deep learning networks. We made the DeepBrainNet model freely available to the community for MRI-based evaluation of brain health in the general population and over the lifespan.

A Multidimensional Neural Maturation Index Reveals Reproducible Developmental Patterns in Children and Adolescents.

Adolescence is a time of extensive neural restructuring, leaving one susceptible to atypical development. Although neural maturation in humans can be measured using functional and structural MRI, the subtle patterns associated with the initial stages of abnormal change may be difficult to identify, particularly at an individual level. Brain age prediction models may have utility in assessing brain development in an individualized manner, as deviations between chronological age and predicted brain age could reflect one's divergence from typical development. Here, we built a support vector regression model to summarize high-dimensional neuroimaging as an index of brain age in both sexes. Using structural and functional MRI data from two large pediatric datasets and a third clinical dataset, we produced and validated a two-dimensional neural maturation index (NMI) that characterizes typical brain maturation patterns and identifies those who deviate from this trajectory. Examination of brain signatures associated with NMI scores revealed that elevated scores were related to significantly lower gray matter volume and significantly higher white matter volume, particularly in high-order regions such as the prefrontal cortex. Additionally, those with higher NMI scores exhibited enhanced connectivity in several functional brain networks, including the default mode network. Analysis of data from a sample of male and female patients with schizophrenia revealed an association between advanced NMI scores and schizophrenia diagnosis in participants aged 16-22, confirming the NMI's utility as a marker of atypicality. Altogether, our findings support the NMI as an individualized, interpretable measure by which neural development in adolescence may be assessed.SIGNIFICANCE STATEMENT The substantial neural restructuring that occurs during adolescence increases one's vulnerability to aberration. A brain index that is capable of capturing one's conformance with typical development will allow for individualized assessment and enhance our understanding of typical and atypical development. In this analysis, we produce a neural maturation index (NMI) using support vector regression and a large pediatric sample. This index generalizes across multiple cohorts and shows potential in the identification of clinical groups. We also implement a novel method for examining the developmental trajectory through data-driven analysis. The signatures identified by the NMI reflect key stages of the extensive neural development that occurs during adolescence and support its utility as a metric of typical brain development.

Predicting Amyloid-ß Levels in Amnestic Mild Cognitive Impairment Using Machine Learning Techniques.

BACKGROUND: Amyloid-ß positivity (Aß+) based on PET imaging is part of the enrollment criteria for many of the clinical trials of Alzheimer's disease (AD), particularly in trials for amyloid-targeted therapy. Predicting Aß positivity prior to PET imaging can decrease unnecessary patient burden and costs of running these trials. OBJECTIVE: The aim of this study was to evaluate the performance of a machine learning model in estimating the individual risk of Aß+ based on gold-standard of PET imaging. METHODS: We used data from an amnestic mild cognitive impairment (aMCI) subset of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to develop and validate the models. The predictors of Aß status included demographic and ApoE4 status in all models plus a combination of neuropsychological tests (NP), MRI volumetrics, and cerebrospinal fluid (CSF) biomarkers. RESULTS: The models that included NP and MRI measures separately showed an area under the receiver operating characteristics (AUC) of 0.74 and 0.72, respectively. However, using NP and MRI measures jointly in the model did not improve prediction. The models including CSF biomarkers significantly outperformed other models with AUCs between 0.89 to 0.92. CONCLUSIONS: Predictive models can be effectively used to identify persons with aMCI likely to be amyloid positive on a subsequent PET scan.

In their own words: stressors facing medical students in the millennial generation.

BACKGROUND: Medical student exposure to stressors is associated with depression, burnout, somatic distress, decreases in empathy, serious thoughts about dropping out of medical school, suicidal ideation, and poor academic performance. Despite this, there have been no recent, multicenter, qualitative studies assessing medical students' perceptions of their greatest stressor(s). OBJECTIVE: The goal of this study was to identify the most significant stressors noted by medical students themselves, in order to inform the development of programs and policies to reduce medical student distress. DESIGN: Medical students from the nine schools in the state of Florida were invited to complete an anonymous online questionnaire assessing wellness and distress. Students were notified that all responses were voluntary and that individual responses would not be linked to themselves or their program. This paper focuses on students' responses to fixed-response items regarding their experience of stress and open-ended responses to the following question: 'What do you consider to be the greatest stressor(s) facing medical students?' Qualitative data were analyzed using the Grounded Theory method of data analysis. RESULTS: Results confirmed the impact of several stressors highlighted in previous studies (e.g., excessive workload, difficulties with studying and time management, conflicts in work-life balance and relationships, medical school peer relations, health concerns, and financial stressors). However, students also reported unique system-level concerns that have not consistently been highlighted in past research (e.g., medical school administrative failures, concerns about lack of assistance with career planning, and assessment-related performance pressure. CONCLUSIONS: Though individually focused interventions have demonstrated some success, medical students self-report stressors that may be better addressed through system-level changes.

Resting-state brain oscillations predict trait-like cognitive styles.

Anecdotal reports suggest the existence of individual differences in peoples' cognitive styles for solving problems, in particular, the tendency to rely on insight (the "aha" phenomenon) versus deliberate analytical thought. We hypothesized that such stable individual differences exist and are associated with trait-like individual differences in resting-state brain activity. We tested this idea by recording participants' resting-state electroencephalograms (RS-EEGs) on 4 occasions over approximately 7 weeks and then tasking them with solving anagrams and compound remote associates problems that are solvable by either strategy. We found that peoples' tendency to solve problems consistently by insight or by analysis spans both tasks and time. Moreover, we discovered trait-like individual differences in the balance between frontal and posterior resting-state brain activity and in temporal-lobe hemispheric asymmetries that predict, at least weeks in advance, the tendency to solve by insight versus analysis. The discovery of an insight-analytic dimension of cognitive style and its neural basis in resting state brain activity suggests new avenues for the development of neuroscience-based methods for intellectual, educational, and vocational assessment.

A Growth-Curve Analysis of the Effects of Future-Thought Priming on Insight and Analytical Problem-Solving.

Research based on construal level theory (CLT) suggests that thinking about the distant future can prime people to solve problems by insight (i.e., an "aha" moment) while thinking about the near future can prime them to solve problems analytically. In this study, we used a novel method to elucidate the time-course of temporal priming effects on creative problem solving. Specifically, we used growth-curve analysis (GCA) to examine the time-course of priming while participants solved a series of brief verbal problems. Participants were tested in two counterbalanced sessions in a within-subject experimental design; one session featured near-future priming and the other featured far-future priming. Our results suggest high-level construal may temporarily enhance analytical thinking; far-future priming caused transient facilitation of analytical solving while near-future priming induced weaker, transient facilitation of insightful solving. However, this effect is short-lived; priming produced no significant differences in the total number of insights and analytical solutions. Given the fleeting nature of these effects, future studies should consider implementing methodology that allows for aspects of the time-course of priming effects to be examined. A method such as GCA may reveal mild effects that would be otherwise missed using other types of analyses.

Adoption of PERILIPIN as a unifying nomenclature for the mammalian PAT-family of intracellular lipid storage droplet proteins.

The PAT family of proteins has been identified in eukaryotic species as diverse as vertebrates, insects, and amebazoa. These proteins share a highly conserved sequence organization and avidity for the surfaces of intracellular, neutral lipid storage droplets. The current nomenclature of the various members lacks consistency and precision, deriving more from historic context than from recognition of evolutionary relationship and shared function. In consultation with the Mouse Genomic Nomenclature Committee, the Human Genome Organization Genomic Nomenclature Committee, and conferees at the 2007 FASEB Conference on Lipid Droplets: Metabolic Consequences of the Storage of Neutral Lipids, we have established a unifying nomenclature for the gene and protein family members. Each gene member will incorporate the root term PERILIPIN (PLIN), the founding gene of the PAT family, with the different genes/proteins numbered sequentially.

Gene Ontology annotations: what they mean and where they come from.

To address the challenges of information integration and retrieval, the computational genomics community increasingly has come to rely on the methodology of creating annotations of scientific literature using terms from controlled structured vocabularies such as the Gene Ontology (GO). Here we address the question of what such annotations signify and of how they are created by working biologists. Our goal is to promote a better understanding of how the results of experiments are captured in annotations, in the hope that this will lead both to better representations of biological reality through annotation and ontology development and to more informed use of GO resources by experimental scientists.